Analysis and processing of in-vivo neural signal for artifact detection and removal

Abstract:

This paper analyses different types of artifacts that
appear in neural recording experiments and thus a method is
proposed to detect and remove artifacts as a part of
preprocessing procedures before information decoding. Through
modeling and data analysis, we reason that artifacts have
different spectrum statistics compared with field potentials and
spikes and the frequency bands of 150-400 Hz and >5 kHz are the
most prospective regions to detect artifacts. A synthesized
database based on recorded neural data and manually labeled
artifacts has been built to allow quantitative evaluations of the
proposed algorithm. Testing results have shown that over >80%
positive detection ratio is achievable for artifacts with magnitude
comparable to neural spikes. Quantitative signal-to-distortion
ratio (SDR) simulation has shown that it is possible to have 10-
30dB SDR improvement at waveform segments that contain
artifacts.

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